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Arjan Hillebrand - One of the best experts on this subject based on the ideXlab platform.
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localization of the Epileptogenic Zone using interictal meg and machine learning in a large cohort of drug resistant epilepsy patients
Frontiers in Neurology, 2018Co-Authors: Ida A Nissen, Cornelis J Stam, Jaap C Reijneveld, Johannes C Baayen, Philip C De Witt Hamer, Sander Idema, Elisabeth C W Van Straaten, Viktor Wottschel, Demetrios N Velis, Arjan HillebrandAbstract:Objective Epilepsy surgery results in seizure freedom in the majority of drug-resistant patients. To improve surgery outcome we studied whether MEG metrics combined with machine learning can improve localization of the Epileptogenic Zone, thereby enhancing the chance of seizure freedom. Methods Presurgical interictal MEG recordings of 94 patients (64 seizure-free >1y post-surgery) were analyzed to extract four metrics in source space: delta power, low-to-high-frequency power ratio, functional connectivity (phase lag index), and minimum spanning tree betweenness centrality. At the group level, we estimated the overlap of the resection area with the five highest values for each metric and determined whether this overlap differed between surgery outcomes. At the individual level, those metrics were used in machine learning classifiers (linear support vector machine (SVM) and random forest) to distinguish between resection and non-resection areas and between surgery outcome groups. Results The highest values, for all metrics, overlapped with the resection area in more than half of the patients, but the overlap did not differ between surgery outcome groups. The classifiers distinguished the resection areas from non-resection areas with 59.94% accuracy (95% confidence interval: 59.67-60.22%) for SVM and 60.34% (59.98-60.71%) for random forest, but could not differentiate seizure-free from not seizure-free patients (43.77% accuracy (42.08-45.45%) for SVM and 49.03% (47.25-50.82%) for random forest). Significance All four metrics localized the resection area but did not distinguish between surgery outcome groups, demonstrating that metrics derived from interictal MEG correspond to expert consensus based on several presurgical evaluation modalities, but do not yet localize the Epileptogenic Zone. Metrics should be improved such that they correspond to the resection area in seizure-free patients but not in patients with persistent seizures. It is important to test such localization strategies at an individual level, for example by using machine learning or individualized models, since surgery is individually tailored.
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an evaluation of kurtosis beamforming in magnetoencephalography to localize the Epileptogenic Zone in drug resistant epilepsy patients
Clinical Neurophysiology, 2018Co-Authors: Michael B H Hall, Ida A Nissen, Elisabeth C W Van Straaten, Paul L Furlong, Caroline Witton, Elaine Foley, Stefano Seri, Arjan HillebrandAbstract:Abstract Objective Kurtosis beamforming is a useful technique for analysing magnetoencephalograpy (MEG) data containing epileptic spikes. However, the implementation varies and few studies measure concordance with subsequently resected areas. We evaluated kurtosis beamforming as a means of localizing spikes in drug-resistant epilepsy patients. Methods We retrospectively applied kurtosis beamforming to MEG recordings of 22 epilepsy patients that had previously been analysed using equivalent current dipole (ECD) fitting. Virtual electrodes were placed in the kurtosis volumetric peaks and visually inspected to select a candidate source. The candidate sources were compared to the ECD localizations and resection areas. Results The kurtosis beamformer produced interpretable localizations in 18/22 patients, of which the candidate source coincided with the resection lobe in 9/13 seizure-free patients and in 3/5 patients with persistent seizures. The sublobar accuracy of the kurtosis beamformer with respect to the resection Zone was higher than ECD (56% and 50%, respectively), however, ECD resulted in a higher lobar accuracy (75%, 67%). Conclusions Kurtosis beamforming may provide additional value when spikes are not clearly discernible on the sensors and support ECD localizations when dipoles are scattered. Significance Kurtosis beamforming should be integrated with existing clinical protocols to assist in localizing the Epileptogenic Zone.
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identifying the Epileptogenic Zone in interictal resting state meg source space networks
Epilepsia, 2017Co-Authors: Ida A Nissen, Cornelis J Stam, Jaap C Reijneveld, Ilse Van Straaten, Eef J Hendriks, Johannes C Baayen, Philip C De Witt Hamer, Sander Idema, Arjan HillebrandAbstract:SummaryObjective In one third of patients, seizures remain after epilepsy surgery, meaning that improved preoperative evaluation methods are needed to identify the Epileptogenic Zone. A potential framework for such a method is network theory, as it can be applied to noninvasive recordings, even in the absence of epileptiform activity. Our aim was to identify the Epileptogenic Zone on the basis of hub status of local brain areas in interictal magnetoencephalography (MEG) networks. Methods Preoperative eyes-closed resting-state MEG recordings were retrospectively analyzed in 22 patients with refractory epilepsy, of whom 14 were seizure-free 1 year after surgery. Beamformer-based time series were reconstructed for 90 cortical and subcortical automated anatomic labeling (AAL) regions of interest (ROIs). Broadband functional connectivity was estimated using the phase lag index in artifact-free epochs without interictal epileptiform abnormalities. A minimum spanning tree was generated to represent the network, and the hub status of each ROI was calculated using betweenness centrality, which indicates the centrality of a node in a network. The correspondence of resection cavity to hub values was evaluated on four levels: resection cavity, lobar, hemisphere, and temporal versus extratemporal areas. Results Hubs were localized within the resection cavity in 8 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (57% sensitivity, 100% specificity, 73% accuracy). Hubs were localized in the lobe of resection in 9 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (64% sensitivity, 100% specificity, 77% accuracy). For the other two levels, the true negatives are unknown; hence, only sensitivity could be determined: hubs coincided with both the resection hemisphere and the resection location (temporal versus extratemporal) in 11 of 14 seizure-free patients (79% sensitivity). Significance Identifying hubs noninvasively before surgery is a valuable approach with the potential of indicating the Epileptogenic Zone in patients without interictal abnormalities.
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Identifying the Epileptogenic Zone in interictal resting‐state MEG source‐space networks
Epilepsia, 2016Co-Authors: Ida A Nissen, Cornelis J Stam, Jaap C Reijneveld, Ilse Van Straaten, Eef J Hendriks, Johannes C Baayen, Philip C De Witt Hamer, Sander Idema, Arjan HillebrandAbstract:SummaryObjective In one third of patients, seizures remain after epilepsy surgery, meaning that improved preoperative evaluation methods are needed to identify the Epileptogenic Zone. A potential framework for such a method is network theory, as it can be applied to noninvasive recordings, even in the absence of epileptiform activity. Our aim was to identify the Epileptogenic Zone on the basis of hub status of local brain areas in interictal magnetoencephalography (MEG) networks. Methods Preoperative eyes-closed resting-state MEG recordings were retrospectively analyzed in 22 patients with refractory epilepsy, of whom 14 were seizure-free 1 year after surgery. Beamformer-based time series were reconstructed for 90 cortical and subcortical automated anatomic labeling (AAL) regions of interest (ROIs). Broadband functional connectivity was estimated using the phase lag index in artifact-free epochs without interictal epileptiform abnormalities. A minimum spanning tree was generated to represent the network, and the hub status of each ROI was calculated using betweenness centrality, which indicates the centrality of a node in a network. The correspondence of resection cavity to hub values was evaluated on four levels: resection cavity, lobar, hemisphere, and temporal versus extratemporal areas. Results Hubs were localized within the resection cavity in 8 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (57% sensitivity, 100% specificity, 73% accuracy). Hubs were localized in the lobe of resection in 9 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (64% sensitivity, 100% specificity, 77% accuracy). For the other two levels, the true negatives are unknown; hence, only sensitivity could be determined: hubs coincided with both the resection hemisphere and the resection location (temporal versus extratemporal) in 11 of 14 seizure-free patients (79% sensitivity). Significance Identifying hubs noninvasively before surgery is a valuable approach with the potential of indicating the Epileptogenic Zone in patients without interictal abnormalities.
Ida A Nissen - One of the best experts on this subject based on the ideXlab platform.
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localization of the Epileptogenic Zone using interictal meg and machine learning in a large cohort of drug resistant epilepsy patients
Frontiers in Neurology, 2018Co-Authors: Ida A Nissen, Cornelis J Stam, Jaap C Reijneveld, Johannes C Baayen, Philip C De Witt Hamer, Sander Idema, Elisabeth C W Van Straaten, Viktor Wottschel, Demetrios N Velis, Arjan HillebrandAbstract:Objective Epilepsy surgery results in seizure freedom in the majority of drug-resistant patients. To improve surgery outcome we studied whether MEG metrics combined with machine learning can improve localization of the Epileptogenic Zone, thereby enhancing the chance of seizure freedom. Methods Presurgical interictal MEG recordings of 94 patients (64 seizure-free >1y post-surgery) were analyzed to extract four metrics in source space: delta power, low-to-high-frequency power ratio, functional connectivity (phase lag index), and minimum spanning tree betweenness centrality. At the group level, we estimated the overlap of the resection area with the five highest values for each metric and determined whether this overlap differed between surgery outcomes. At the individual level, those metrics were used in machine learning classifiers (linear support vector machine (SVM) and random forest) to distinguish between resection and non-resection areas and between surgery outcome groups. Results The highest values, for all metrics, overlapped with the resection area in more than half of the patients, but the overlap did not differ between surgery outcome groups. The classifiers distinguished the resection areas from non-resection areas with 59.94% accuracy (95% confidence interval: 59.67-60.22%) for SVM and 60.34% (59.98-60.71%) for random forest, but could not differentiate seizure-free from not seizure-free patients (43.77% accuracy (42.08-45.45%) for SVM and 49.03% (47.25-50.82%) for random forest). Significance All four metrics localized the resection area but did not distinguish between surgery outcome groups, demonstrating that metrics derived from interictal MEG correspond to expert consensus based on several presurgical evaluation modalities, but do not yet localize the Epileptogenic Zone. Metrics should be improved such that they correspond to the resection area in seizure-free patients but not in patients with persistent seizures. It is important to test such localization strategies at an individual level, for example by using machine learning or individualized models, since surgery is individually tailored.
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an evaluation of kurtosis beamforming in magnetoencephalography to localize the Epileptogenic Zone in drug resistant epilepsy patients
Clinical Neurophysiology, 2018Co-Authors: Michael B H Hall, Ida A Nissen, Elisabeth C W Van Straaten, Paul L Furlong, Caroline Witton, Elaine Foley, Stefano Seri, Arjan HillebrandAbstract:Abstract Objective Kurtosis beamforming is a useful technique for analysing magnetoencephalograpy (MEG) data containing epileptic spikes. However, the implementation varies and few studies measure concordance with subsequently resected areas. We evaluated kurtosis beamforming as a means of localizing spikes in drug-resistant epilepsy patients. Methods We retrospectively applied kurtosis beamforming to MEG recordings of 22 epilepsy patients that had previously been analysed using equivalent current dipole (ECD) fitting. Virtual electrodes were placed in the kurtosis volumetric peaks and visually inspected to select a candidate source. The candidate sources were compared to the ECD localizations and resection areas. Results The kurtosis beamformer produced interpretable localizations in 18/22 patients, of which the candidate source coincided with the resection lobe in 9/13 seizure-free patients and in 3/5 patients with persistent seizures. The sublobar accuracy of the kurtosis beamformer with respect to the resection Zone was higher than ECD (56% and 50%, respectively), however, ECD resulted in a higher lobar accuracy (75%, 67%). Conclusions Kurtosis beamforming may provide additional value when spikes are not clearly discernible on the sensors and support ECD localizations when dipoles are scattered. Significance Kurtosis beamforming should be integrated with existing clinical protocols to assist in localizing the Epileptogenic Zone.
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identifying the Epileptogenic Zone in interictal resting state meg source space networks
Epilepsia, 2017Co-Authors: Ida A Nissen, Cornelis J Stam, Jaap C Reijneveld, Ilse Van Straaten, Eef J Hendriks, Johannes C Baayen, Philip C De Witt Hamer, Sander Idema, Arjan HillebrandAbstract:SummaryObjective In one third of patients, seizures remain after epilepsy surgery, meaning that improved preoperative evaluation methods are needed to identify the Epileptogenic Zone. A potential framework for such a method is network theory, as it can be applied to noninvasive recordings, even in the absence of epileptiform activity. Our aim was to identify the Epileptogenic Zone on the basis of hub status of local brain areas in interictal magnetoencephalography (MEG) networks. Methods Preoperative eyes-closed resting-state MEG recordings were retrospectively analyzed in 22 patients with refractory epilepsy, of whom 14 were seizure-free 1 year after surgery. Beamformer-based time series were reconstructed for 90 cortical and subcortical automated anatomic labeling (AAL) regions of interest (ROIs). Broadband functional connectivity was estimated using the phase lag index in artifact-free epochs without interictal epileptiform abnormalities. A minimum spanning tree was generated to represent the network, and the hub status of each ROI was calculated using betweenness centrality, which indicates the centrality of a node in a network. The correspondence of resection cavity to hub values was evaluated on four levels: resection cavity, lobar, hemisphere, and temporal versus extratemporal areas. Results Hubs were localized within the resection cavity in 8 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (57% sensitivity, 100% specificity, 73% accuracy). Hubs were localized in the lobe of resection in 9 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (64% sensitivity, 100% specificity, 77% accuracy). For the other two levels, the true negatives are unknown; hence, only sensitivity could be determined: hubs coincided with both the resection hemisphere and the resection location (temporal versus extratemporal) in 11 of 14 seizure-free patients (79% sensitivity). Significance Identifying hubs noninvasively before surgery is a valuable approach with the potential of indicating the Epileptogenic Zone in patients without interictal abnormalities.
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Identifying the Epileptogenic Zone in interictal resting‐state MEG source‐space networks
Epilepsia, 2016Co-Authors: Ida A Nissen, Cornelis J Stam, Jaap C Reijneveld, Ilse Van Straaten, Eef J Hendriks, Johannes C Baayen, Philip C De Witt Hamer, Sander Idema, Arjan HillebrandAbstract:SummaryObjective In one third of patients, seizures remain after epilepsy surgery, meaning that improved preoperative evaluation methods are needed to identify the Epileptogenic Zone. A potential framework for such a method is network theory, as it can be applied to noninvasive recordings, even in the absence of epileptiform activity. Our aim was to identify the Epileptogenic Zone on the basis of hub status of local brain areas in interictal magnetoencephalography (MEG) networks. Methods Preoperative eyes-closed resting-state MEG recordings were retrospectively analyzed in 22 patients with refractory epilepsy, of whom 14 were seizure-free 1 year after surgery. Beamformer-based time series were reconstructed for 90 cortical and subcortical automated anatomic labeling (AAL) regions of interest (ROIs). Broadband functional connectivity was estimated using the phase lag index in artifact-free epochs without interictal epileptiform abnormalities. A minimum spanning tree was generated to represent the network, and the hub status of each ROI was calculated using betweenness centrality, which indicates the centrality of a node in a network. The correspondence of resection cavity to hub values was evaluated on four levels: resection cavity, lobar, hemisphere, and temporal versus extratemporal areas. Results Hubs were localized within the resection cavity in 8 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (57% sensitivity, 100% specificity, 73% accuracy). Hubs were localized in the lobe of resection in 9 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (64% sensitivity, 100% specificity, 77% accuracy). For the other two levels, the true negatives are unknown; hence, only sensitivity could be determined: hubs coincided with both the resection hemisphere and the resection location (temporal versus extratemporal) in 11 of 14 seizure-free patients (79% sensitivity). Significance Identifying hubs noninvasively before surgery is a valuable approach with the potential of indicating the Epileptogenic Zone in patients without interictal abnormalities.
Elysa Widjaja - One of the best experts on this subject based on the ideXlab platform.
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magnetoencephalography spike sources interrelate the extensive Epileptogenic Zone of tuberous sclerosis complex
Epilepsy Research, 2016Co-Authors: Tohru Okanishi, Ayako Ochi, Cristina Go, Tomoyuki Akiyama, Ellen Mayo, Yasunori Honda, Chihiro Uedakawada, Midori Nakajima, Yoichiro Homma, Elysa WidjajaAbstract:Abstract Objective We hypothesized that the extensive epileptic network in patients with tuberous sclerosis complex (TSC) manifests as clustered and scattered distributions of magnetoencephalography spike sources (MEGSS). Methods We retrospectively analyzed pre-surgical MEG in 15 patients with TSC. We performed single moving dipole analysis to localize and classify clustered and scattered MEGSS. We compared the number of electrodes within the resected area (RA) and the proportions of clustered and scattered MEGSS within RA with the seizure outcome. Results The number of electrodes within RA ranged from 29 to 83 (mean = 51). The MEGSS were distributed over multiple lobes (3–8; mean = 5.9) and bilaterally in 14 patients. Clusters of MEGSS ranged from 1 to 4 (mean = 1.4). The number of MEGSS ranged in total from 28 to 139 (mean = 70); in the clusters, 10–128 (mean = 49); and in the scatters, 0–45 (mean = 21). Four patients achieved an Engel class I surgical outcome, four, a class II outcome; five, a class III outcome; and two, a class IV outcome. The proportion of MEGSS ranged in total from 0 to 92% (mean = 57%) within RA; 0–100% (mean = 67%) in the resection hemisphere; 0–100% (mean = 63%) in the clusters; and 0–81% (mean = 28%) in the scatters. Univariate ordinal logistic regression analyses showed that the proportion of scattered MEGSS within RA (p = 0.049) significantly correlated with seizure outcomes. Multivariate analyses using three covariates (number of electrodes, proportions of clustered and scattered MEGSS within RA) showed that only the proportion of scattered MEGSS within RA significantly correlated with seizure outcomes (p = 0.016). Significance MEG data showed a wide distribution of multilobar MEGSS in patients with TSC. The seizure outcome was not related to the clustered MEGSS within RA, since the grids were essentially planned to cover and resect the clustered MEGSS surrounding tubers. The maximal possible resection of scattered MEGSS correlated with improved seizure outcome in TSC. Some parts of the Epileptogenic Zone disrupted by multiple tubers did not have a sufficiently large area to produce clustered MEGSS. Although the wide distribution of scattered MEGSS is not interpreted as Epileptogenic, they might be interrelated with clustered MEGSS to project a complex epilepsy network and be part of the extensive Epileptogenic Zones found in TSC.
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magnetoencephalography helps delineate the extent of the Epileptogenic Zone for surgical planning in children with intractable epilepsy due to porencephalic cyst encephalomalacia
Journal of Neurosurgery, 2014Co-Authors: Odeya Bennettback, Elysa Widjaja, Ayako Ochi, James T Rutka, Shohei Nambu, Akio Kamiya, Cristina Go, Sylvester H Chuang, James M Drake, Carter O SneadAbstract:Object Porencephalic cyst/encephalomalacia (PC/E) is a brain lesion caused by ischemic insult or hemorrhage. The authors evaluated magnetoencephalography (MEG) spike sources (MEGSS) to localize the Epileptogenic Zone in children with intractable epilepsy secondary to PC/E. Methods The authors retrospectively studied 13 children with intractable epilepsy secondary to PC/E (5 girls and 8 boys, age range 1.8–15 years), who underwent prolonged scalp video-electroencephalography (EEG), MRI, and MEG. Interictal MEGSS locations were compared with the ictal and interictal Zones as determined from scalp video-EEG. Results Magnetic resonance imaging showed PC/E in extratemporal lobes in 3 patients, within the temporal lobe in 2 patients, and in both temporal and extratemporal lobes in 8 patients. Magnetoencephalographic spike sources were asymmetrically clustered at the margin of PC/E in all 13 patients. One cluster of MEGSS was observed in 11 patients, 2 clusters in 1 patient, and 3 clusters in 1 patient. Ictal EE...
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diffusion tensor imaging assessment of the Epileptogenic Zone in children with localization related epilepsy
American Journal of Neuroradiology, 2011Co-Authors: Elysa Widjaja, Hiroshi Otsubo, O Carter Snead, S Geibprasert, Sina Zarei MahmoodabadiAbstract:BACKGROUND AND PURPOSE: Patients with MR imaging-negative epilepsy could have subtle FCD. Our aim was to determine if structural changes could be identified by using DTI in children with intractable epilepsy, from MR imaging-visible FCD and MR imaging-negative localization-related epilepsy, that were concordant with the Epileptogenic Zone as defined by using the MEG dipole cluster. MATERIALS AND METHODS: Eight children with MR imaging-visible FCD and 16 with MR imaging-negative epilepsy underwent DTI and MEG. Twenty-six age-matched healthy children underwent DTI. Analysis was performed on controls across individual patients. Agreement between the location of DTI abnormalities and FCD and MEG dipole clusters was assessed. RESULTS: In patients with MR imaging-visible FCD, abnormal FA, MD, λ 1 , λ 2 , and λ 3 were lobar concordant with the MEG dipole cluster in 4/8 (50.0%), 5/8 (62.5%), 3/8 (37.5%), 6/8 (75.0%), and 5/8 (62.5%), respectively. In patients with MR imaging-visible FCD, abnormal FA, MD, λ 1 , λ 2 , and λ 3 overlapped the x-, y-, and z-axes of the MEG dipole cluster in 1/8 (12.5%), 4/8 (50%), 4/8 (50%), 6/8 (75%), and 4/8 (50%), respectively, and with FCD in 1/8 (12.5%), 3/8 (37.5%), 0/8 (0%), 3/8 (37.5%), and 1/8 (12.5%), respectively. In patients with MR imaging-negative epilepsy, abnormal FA, MD, λ 1 , λ 2 , and λ 3 were lobar-concordant with the MEG dipole cluster in 11/16 (68.8%), 11/16 (68.8%), 8/16 (50.0%), 10/16 (62.5%), and 10/16 (62.5%), respectively, and overlapped the x-, y-, and z-axes of the MEG dipole cluster in 9/16 (56.3%), 10/16 (62.5%), 8/16 (50%), 8/16 (50%), and 8/16 (50%), respectively. There was no significant difference between abnormal DTI lobar concordance with the MEG dipole cluster in patients with MR imaging-visible FCD and MR imaging-negative epilepsy. CONCLUSIONS: White matter changes can be detected with DTI in children with MR imaging-visible FCD and MR imaging-negative epilepsy, which were concordant with the Epileptogenic Zone in more than half of the patients.
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diffusion tensor imaging identifies changes in normal appearing white matter within the Epileptogenic Zone in tuberous sclerosis complex
Epilepsy Research, 2010Co-Authors: Elysa Widjaja, Gustavo Simao, Sina Zarei Mahmoodabadi, Ayako Ochi, Carter O Snead, James T Rutka, Hiroshi OtsuboAbstract:Summary Purpose To evaluate diffusion tensor imaging (DTI) indices of (i) cortical tubers and (ii) normal-appearing subcortical white matter adjacent to cortical tubers within the Epileptogenic Zone and non-Epileptogenic Zone. Methods Twelve children with tuberous sclerosis complex underwent MRI, DTI and magnetoencephalography (MEG). Regions of interest (ROIs) were placed within cortical tubers and normal-appearing subcortical white matter adjacent to cortical tubers within MEG identified Epileptogenic Zone and non-Epileptogenic Zone. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity ( λ || ) and radial diffusivity ( λ ⊥ ) were calculated. Results 26 out of 104 cortical tubers were in the Epileptogenic Zone. FA of cortical tubers in the Epileptogenic Zone was significantly lower than non-Epileptogenic Zone ( p =0.015). There were no significant differences between MD ( p =0.896), λ || ( p =0.672) and λ ⊥ ( p =0.651) of cortical tubers in the Epileptogenic and non-Epileptogenic Zone. In normal-appearing subcortical white matter within the Epileptogenic Zone, FA was lower ( p =0.001) and λ ⊥ ( p =0.011) was higher than non-Epileptogenic Zone. There were no significant differences between MD ( p =0.110) and λ || ( p =0.735) of normal-appearing subcortical white matter within the Epileptogenic and non-Epileptogenic Zone. Conclusion DTI changes in normal-appearing white matter within the Epileptogenic Zone could represent abnormal white matter related to MRI-occult dysplastic cortex or ictal/interictal activity.
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Diffusion tensor imaging identifies changes in normal-appearing white matter within the Epileptogenic Zone in tuberous sclerosis complex.
Epilepsy research, 2010Co-Authors: Elysa Widjaja, Gustavo Simao, Sina Zarei Mahmoodabadi, Ayako Ochi, James T Rutka, O Carter Snead, Hiroshi OtsuboAbstract:To evaluate diffusion tensor imaging (DTI) indices of (i) cortical tubers and (ii) normal-appearing subcortical white matter adjacent to cortical tubers within the Epileptogenic Zone and non-Epileptogenic Zone. Twelve children with tuberous sclerosis complex underwent MRI, DTI and magnetoencephalography (MEG). Regions of interest (ROIs) were placed within cortical tubers and normal-appearing subcortical white matter adjacent to cortical tubers within MEG identified Epileptogenic Zone and non-Epileptogenic Zone. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (lambda(parallel)) and radial diffusivity (lambda(perpendicular)) were calculated. 26 out of 104 cortical tubers were in the Epileptogenic Zone. FA of cortical tubers in the Epileptogenic Zone was significantly lower than non-Epileptogenic Zone (p=0.015). There were no significant differences between MD (p=0.896), lambda(parallel) (p=0.672) and lambda(perpendicular) (p=0.651) of cortical tubers in the Epileptogenic and non-Epileptogenic Zone. In normal-appearing subcortical white matter within the Epileptogenic Zone, FA was lower (p=0.001) and lambda(perpendicular) (p=0.011) was higher than non-Epileptogenic Zone. There were no significant differences between MD (p=0.110) and lambda(parallel) (p=0.735) of normal-appearing subcortical white matter within the Epileptogenic and non-Epileptogenic Zone. DTI changes in normal-appearing white matter within the Epileptogenic Zone could represent abnormal white matter related to MRI-occult dysplastic cortex or ictal/interictal activity. Copyright 2010 Elsevier B.V. All rights reserved.
Hiroshi Otsubo - One of the best experts on this subject based on the ideXlab platform.
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The Epileptogenic Zone in pharmaco-resistant temporal lobe epilepsy with amygdala enlargement
Epileptic Disorders, 2019Co-Authors: Hiroharu Suzuki, Hiroshi Otsubo, Hidenori Sugano, Madoka Nakajima, Takuma Higo, Yasushi Iimura, Takumi Mitsuhashi, Keiko Fusegi, Akiyoshi Kakita, Hajime AraiAbstract:Temporal lobe epilepsy with amygdala enlargement (TLE-AE) has been considered a subtype of TLE. We evaluated the Epileptogenic Zone in patients with TLE-AE, who underwent intracranial video-EEG (ivEEG) and/or intraoperative electrocorticography (ioECoG) as well as epilepsy surgery. Eleven patients with TLE-AE were enrolled and investigated based on seizure profile, volumetric MRI, the Wechsler Memory Scale-Revised (WMS-R), the location of seizure onset Zone (SOZ) and irritative Zone (IZ) based on ivEEG (n=8), the location of interictal epileptiform discharges (IEDs) based on ioECoG (11), surgical procedure, and seizure outcome. The mean age at seizure onset was 34.9 years (range: 23-57). The mean duration of seizures was 5.0 years (range: 1-10). The number of AEDs was 2.3 (range: 1-5). The mean seizure frequency was nine per month (range: 1-30/month). All patients presented with focal impaired awareness seizures with (n=9) and without (2) secondary generalized convulsions. Volumetric MRI analysis showed unilateral enlarged amygdala with statistical significance (p
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diffusion tensor imaging assessment of the Epileptogenic Zone in children with localization related epilepsy
American Journal of Neuroradiology, 2011Co-Authors: Elysa Widjaja, Hiroshi Otsubo, O Carter Snead, S Geibprasert, Sina Zarei MahmoodabadiAbstract:BACKGROUND AND PURPOSE: Patients with MR imaging-negative epilepsy could have subtle FCD. Our aim was to determine if structural changes could be identified by using DTI in children with intractable epilepsy, from MR imaging-visible FCD and MR imaging-negative localization-related epilepsy, that were concordant with the Epileptogenic Zone as defined by using the MEG dipole cluster. MATERIALS AND METHODS: Eight children with MR imaging-visible FCD and 16 with MR imaging-negative epilepsy underwent DTI and MEG. Twenty-six age-matched healthy children underwent DTI. Analysis was performed on controls across individual patients. Agreement between the location of DTI abnormalities and FCD and MEG dipole clusters was assessed. RESULTS: In patients with MR imaging-visible FCD, abnormal FA, MD, λ 1 , λ 2 , and λ 3 were lobar concordant with the MEG dipole cluster in 4/8 (50.0%), 5/8 (62.5%), 3/8 (37.5%), 6/8 (75.0%), and 5/8 (62.5%), respectively. In patients with MR imaging-visible FCD, abnormal FA, MD, λ 1 , λ 2 , and λ 3 overlapped the x-, y-, and z-axes of the MEG dipole cluster in 1/8 (12.5%), 4/8 (50%), 4/8 (50%), 6/8 (75%), and 4/8 (50%), respectively, and with FCD in 1/8 (12.5%), 3/8 (37.5%), 0/8 (0%), 3/8 (37.5%), and 1/8 (12.5%), respectively. In patients with MR imaging-negative epilepsy, abnormal FA, MD, λ 1 , λ 2 , and λ 3 were lobar-concordant with the MEG dipole cluster in 11/16 (68.8%), 11/16 (68.8%), 8/16 (50.0%), 10/16 (62.5%), and 10/16 (62.5%), respectively, and overlapped the x-, y-, and z-axes of the MEG dipole cluster in 9/16 (56.3%), 10/16 (62.5%), 8/16 (50%), 8/16 (50%), and 8/16 (50%), respectively. There was no significant difference between abnormal DTI lobar concordance with the MEG dipole cluster in patients with MR imaging-visible FCD and MR imaging-negative epilepsy. CONCLUSIONS: White matter changes can be detected with DTI in children with MR imaging-visible FCD and MR imaging-negative epilepsy, which were concordant with the Epileptogenic Zone in more than half of the patients.
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diffusion tensor imaging identifies changes in normal appearing white matter within the Epileptogenic Zone in tuberous sclerosis complex
Epilepsy Research, 2010Co-Authors: Elysa Widjaja, Gustavo Simao, Sina Zarei Mahmoodabadi, Ayako Ochi, Carter O Snead, James T Rutka, Hiroshi OtsuboAbstract:Summary Purpose To evaluate diffusion tensor imaging (DTI) indices of (i) cortical tubers and (ii) normal-appearing subcortical white matter adjacent to cortical tubers within the Epileptogenic Zone and non-Epileptogenic Zone. Methods Twelve children with tuberous sclerosis complex underwent MRI, DTI and magnetoencephalography (MEG). Regions of interest (ROIs) were placed within cortical tubers and normal-appearing subcortical white matter adjacent to cortical tubers within MEG identified Epileptogenic Zone and non-Epileptogenic Zone. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity ( λ || ) and radial diffusivity ( λ ⊥ ) were calculated. Results 26 out of 104 cortical tubers were in the Epileptogenic Zone. FA of cortical tubers in the Epileptogenic Zone was significantly lower than non-Epileptogenic Zone ( p =0.015). There were no significant differences between MD ( p =0.896), λ || ( p =0.672) and λ ⊥ ( p =0.651) of cortical tubers in the Epileptogenic and non-Epileptogenic Zone. In normal-appearing subcortical white matter within the Epileptogenic Zone, FA was lower ( p =0.001) and λ ⊥ ( p =0.011) was higher than non-Epileptogenic Zone. There were no significant differences between MD ( p =0.110) and λ || ( p =0.735) of normal-appearing subcortical white matter within the Epileptogenic and non-Epileptogenic Zone. Conclusion DTI changes in normal-appearing white matter within the Epileptogenic Zone could represent abnormal white matter related to MRI-occult dysplastic cortex or ictal/interictal activity.
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Diffusion tensor imaging identifies changes in normal-appearing white matter within the Epileptogenic Zone in tuberous sclerosis complex.
Epilepsy research, 2010Co-Authors: Elysa Widjaja, Gustavo Simao, Sina Zarei Mahmoodabadi, Ayako Ochi, James T Rutka, O Carter Snead, Hiroshi OtsuboAbstract:To evaluate diffusion tensor imaging (DTI) indices of (i) cortical tubers and (ii) normal-appearing subcortical white matter adjacent to cortical tubers within the Epileptogenic Zone and non-Epileptogenic Zone. Twelve children with tuberous sclerosis complex underwent MRI, DTI and magnetoencephalography (MEG). Regions of interest (ROIs) were placed within cortical tubers and normal-appearing subcortical white matter adjacent to cortical tubers within MEG identified Epileptogenic Zone and non-Epileptogenic Zone. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (lambda(parallel)) and radial diffusivity (lambda(perpendicular)) were calculated. 26 out of 104 cortical tubers were in the Epileptogenic Zone. FA of cortical tubers in the Epileptogenic Zone was significantly lower than non-Epileptogenic Zone (p=0.015). There were no significant differences between MD (p=0.896), lambda(parallel) (p=0.672) and lambda(perpendicular) (p=0.651) of cortical tubers in the Epileptogenic and non-Epileptogenic Zone. In normal-appearing subcortical white matter within the Epileptogenic Zone, FA was lower (p=0.001) and lambda(perpendicular) (p=0.011) was higher than non-Epileptogenic Zone. There were no significant differences between MD (p=0.110) and lambda(parallel) (p=0.735) of normal-appearing subcortical white matter within the Epileptogenic and non-Epileptogenic Zone. DTI changes in normal-appearing white matter within the Epileptogenic Zone could represent abnormal white matter related to MRI-occult dysplastic cortex or ictal/interictal activity. Copyright 2010 Elsevier B.V. All rights reserved.
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single and multiple clusters of magnetoencephalographic dipoles in neocortical epilepsy significance in characterizing the Epileptogenic Zone
Epilepsia, 2006Co-Authors: Makoto Oishi, Shigeki Kameyama, Hiroshi Masuda, Jun Tohyama, Osamu Kanazawa, Mutsuo Sasagawa, Hiroshi OtsuboAbstract:Summary: Purpose: To characterize the Epileptogenic Zone in neocortical epilepsy (NE) by using magnetoencephalography (MEG). Methods: We defined and compared locations of single and multiple clusters of equivalent current dipoles (ECDs) for interictal spikes with MRI findings, ictal-onset Zones (IOZs) from subdural electroencephalography (SDEEG), resected areas, and postsurgical outcomes of 20 patients who underwent cortical resection for medically intractable NE. Results: Fourteen patients had single clusters; six had multiple clusters. Overlap of clusters and IOZs defined group A (nine patients), in which a single cluster coincided with the IOZ; group B1 (four patients), in which a single cluster was within or partially overlapped the IOZ; group B2 (five patients), in which multiple-cluster sections overlapped IOZs; group C (two patients; one single; one multiple), in which no overlap was seen. More single clusters (nine of 14) than multiple clusters (none of six) coincided with the IOZ (p = 0.014). More patients with single clusters (10 of 14) than patients with multiple clusters (one of six) had seizure-free outcomes (p = 0.049). Eight of nine patients in group A, versus three of 11 in groups B1, B2, and C, achieved seizure-free outcomes (p = 0.0098). Correlations between MRI findings and postsurgical outcomes were not statistically significant; eight of 13 patients with single lesions, one of four with no lesions, and two of three with multifocal lesions had seizure-free outcomes. Conclusions: In neocortical epilepsy, MEG ECD clusters correlated with SDEEG IOZs. Single clusters indicated discrete Epileptogenic Zones that required complete resection for seizure-free outcome. Multiple clusters necessitated that the multiple or extensive Epileptogenic Zones be completely identified and delineated by SDEEG.
Jaap C Reijneveld - One of the best experts on this subject based on the ideXlab platform.
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localization of the Epileptogenic Zone using interictal meg and machine learning in a large cohort of drug resistant epilepsy patients
Frontiers in Neurology, 2018Co-Authors: Ida A Nissen, Cornelis J Stam, Jaap C Reijneveld, Johannes C Baayen, Philip C De Witt Hamer, Sander Idema, Elisabeth C W Van Straaten, Viktor Wottschel, Demetrios N Velis, Arjan HillebrandAbstract:Objective Epilepsy surgery results in seizure freedom in the majority of drug-resistant patients. To improve surgery outcome we studied whether MEG metrics combined with machine learning can improve localization of the Epileptogenic Zone, thereby enhancing the chance of seizure freedom. Methods Presurgical interictal MEG recordings of 94 patients (64 seizure-free >1y post-surgery) were analyzed to extract four metrics in source space: delta power, low-to-high-frequency power ratio, functional connectivity (phase lag index), and minimum spanning tree betweenness centrality. At the group level, we estimated the overlap of the resection area with the five highest values for each metric and determined whether this overlap differed between surgery outcomes. At the individual level, those metrics were used in machine learning classifiers (linear support vector machine (SVM) and random forest) to distinguish between resection and non-resection areas and between surgery outcome groups. Results The highest values, for all metrics, overlapped with the resection area in more than half of the patients, but the overlap did not differ between surgery outcome groups. The classifiers distinguished the resection areas from non-resection areas with 59.94% accuracy (95% confidence interval: 59.67-60.22%) for SVM and 60.34% (59.98-60.71%) for random forest, but could not differentiate seizure-free from not seizure-free patients (43.77% accuracy (42.08-45.45%) for SVM and 49.03% (47.25-50.82%) for random forest). Significance All four metrics localized the resection area but did not distinguish between surgery outcome groups, demonstrating that metrics derived from interictal MEG correspond to expert consensus based on several presurgical evaluation modalities, but do not yet localize the Epileptogenic Zone. Metrics should be improved such that they correspond to the resection area in seizure-free patients but not in patients with persistent seizures. It is important to test such localization strategies at an individual level, for example by using machine learning or individualized models, since surgery is individually tailored.
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identifying the Epileptogenic Zone in interictal resting state meg source space networks
Epilepsia, 2017Co-Authors: Ida A Nissen, Cornelis J Stam, Jaap C Reijneveld, Ilse Van Straaten, Eef J Hendriks, Johannes C Baayen, Philip C De Witt Hamer, Sander Idema, Arjan HillebrandAbstract:SummaryObjective In one third of patients, seizures remain after epilepsy surgery, meaning that improved preoperative evaluation methods are needed to identify the Epileptogenic Zone. A potential framework for such a method is network theory, as it can be applied to noninvasive recordings, even in the absence of epileptiform activity. Our aim was to identify the Epileptogenic Zone on the basis of hub status of local brain areas in interictal magnetoencephalography (MEG) networks. Methods Preoperative eyes-closed resting-state MEG recordings were retrospectively analyzed in 22 patients with refractory epilepsy, of whom 14 were seizure-free 1 year after surgery. Beamformer-based time series were reconstructed for 90 cortical and subcortical automated anatomic labeling (AAL) regions of interest (ROIs). Broadband functional connectivity was estimated using the phase lag index in artifact-free epochs without interictal epileptiform abnormalities. A minimum spanning tree was generated to represent the network, and the hub status of each ROI was calculated using betweenness centrality, which indicates the centrality of a node in a network. The correspondence of resection cavity to hub values was evaluated on four levels: resection cavity, lobar, hemisphere, and temporal versus extratemporal areas. Results Hubs were localized within the resection cavity in 8 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (57% sensitivity, 100% specificity, 73% accuracy). Hubs were localized in the lobe of resection in 9 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (64% sensitivity, 100% specificity, 77% accuracy). For the other two levels, the true negatives are unknown; hence, only sensitivity could be determined: hubs coincided with both the resection hemisphere and the resection location (temporal versus extratemporal) in 11 of 14 seizure-free patients (79% sensitivity). Significance Identifying hubs noninvasively before surgery is a valuable approach with the potential of indicating the Epileptogenic Zone in patients without interictal abnormalities.
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Identifying the Epileptogenic Zone in interictal resting‐state MEG source‐space networks
Epilepsia, 2016Co-Authors: Ida A Nissen, Cornelis J Stam, Jaap C Reijneveld, Ilse Van Straaten, Eef J Hendriks, Johannes C Baayen, Philip C De Witt Hamer, Sander Idema, Arjan HillebrandAbstract:SummaryObjective In one third of patients, seizures remain after epilepsy surgery, meaning that improved preoperative evaluation methods are needed to identify the Epileptogenic Zone. A potential framework for such a method is network theory, as it can be applied to noninvasive recordings, even in the absence of epileptiform activity. Our aim was to identify the Epileptogenic Zone on the basis of hub status of local brain areas in interictal magnetoencephalography (MEG) networks. Methods Preoperative eyes-closed resting-state MEG recordings were retrospectively analyzed in 22 patients with refractory epilepsy, of whom 14 were seizure-free 1 year after surgery. Beamformer-based time series were reconstructed for 90 cortical and subcortical automated anatomic labeling (AAL) regions of interest (ROIs). Broadband functional connectivity was estimated using the phase lag index in artifact-free epochs without interictal epileptiform abnormalities. A minimum spanning tree was generated to represent the network, and the hub status of each ROI was calculated using betweenness centrality, which indicates the centrality of a node in a network. The correspondence of resection cavity to hub values was evaluated on four levels: resection cavity, lobar, hemisphere, and temporal versus extratemporal areas. Results Hubs were localized within the resection cavity in 8 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (57% sensitivity, 100% specificity, 73% accuracy). Hubs were localized in the lobe of resection in 9 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (64% sensitivity, 100% specificity, 77% accuracy). For the other two levels, the true negatives are unknown; hence, only sensitivity could be determined: hubs coincided with both the resection hemisphere and the resection location (temporal versus extratemporal) in 11 of 14 seizure-free patients (79% sensitivity). Significance Identifying hubs noninvasively before surgery is a valuable approach with the potential of indicating the Epileptogenic Zone in patients without interictal abnormalities.